This is a graded exercise and is to be completed on an individual basis. You may consult written reference material. However, you are not to discuss any aspect of this assignment with anyone other than your instructor. Clearly, student-teacher relationships are built on trust. Acts that violate this trust undermine the educational process and compromise the integrity of the perpetrator. Don't cheat. Don't compromise your integrity. To do so invalidates the very purpose which likely motivated you to undertake this course—to learn, to become a better decision maker, to broaden your perspective, and to increase your skill set. This assignment is due Wednesday of Week 8. Problem Statement: Suppose we are trying to explain variations in salaries for technicians in a particular field of work. The worksheet Salary Data in the Excel workbook Data_MATH325_Project3.xlsx, which is accessible via the Project 3 assignment in the Assignments section of our classroom, contains the annual salaries for 200 technicians. It also shows how many years of experience each technician has, as well as his or her education level. There are four education levels, as explained in the comment in cell D3 on the Salary Data worksheet. In contemplating how to examine the relationship between salary and years of experience and education level, suppose three thoughts come to mind: The years of experience and the amount of education a technician has should be related to his or her salary. In this case, one may be tempted to regress Salary linearly versus the two variables: YrsExper and EducLev. A technician's salary depends on his or her years of experience, but when it comes to education all that really matters is whether or not the technician has a college degree. In this case, one may want to regress Salary versus YrsExper and a dummy variable indicating whether or not a technician has a college degree. When it comes to education, different levels of education may impact a technician's salary level. In this case, one may want to regress Salary linearly versus YrsExper and dummy variables that appropriately account for the different education levels. Using the regression procedure provided in the Analysis Toolpak in Excel, run the indicated regressions associated with the three scenarios above. Save the results of these three regression analyses in a worksheet called RegrOutput in an Excel workbook called YOURNAME_Proj3Analysis. Using text boxes, as required, clearly label your output so that your instructor can easily identify the output associated with each of the three analyses. Then, create either a pdf or Microsoft Word document called YOURNAME_Proj3Summary and place your responses to the following on it: (10 points) What are the estimated regression equations associated with the three scenarios discussed above? Clearly list and identify them. (10 points) Interpret the meaning of each of the estimated regression equations you found. Discuss the usefulness of the information that is provided by each of the estimated regression equations and any limitations associated with them. (30 points) Specify which of the equations you prefer and why. Be specific and definitively justify your selection. (20 points) Discuss whether or not the results associated with your preferred regression model contradict the average salary results reported in the pivot table provided on the Salary Table worksheet in the Excel workbook MATH325_Project3.xlsx. Be specific and present evidence to support your conclusions. (10 points) Using your preferred regression equation, calculate a predicted salary and a 95% prediction interval for each of the four workers listed on the Salary Predict worksheet in the Excel workbook MATH325_Project3.xlsx. (20 points)
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